I gave a fun (and useful?) lightning talk at Startupfest Montreal 2016 and Ignite Seattle entitled Guaranteed Successful Design. Here’s the slide deck with notes and video from Ignite Seattle. I hope you find it useful and/or entertaining.
The Ignite version (5 minutes):
Please enjoy content based on my Four Pillars of Visualization design principles. I hope you find it useful. The best way to reach me to provide errata and feedback is on twitter.
The Four Pillars are:
- Purpose (the why)
- Content (the what)
- Structure (the how)
- Formatting (everything else)
Here are the resources. I’ll update this page as new material comes online. Everything is free.
- A five-part webcast series on good visualization design, based on the Four Pillars, and PDFs of the slide decks: 1, 2, 3, 4, 5
- Concise blog posts discussing the Four Pillars. The depth here is roughly equivalent to what I cover in the standard Four Pillars lecture (as seen above).
- Longer whitepapers that go more in-depth on how to make good design choices.
- A slide deck from spring 2016 covering the process, which works for a lot of styles of communication, not just visualization.
Here are the various, videos, recordings, transcripts, notes, and slide decks from some of the talks I gave in the first half of 2012. With one exception, all of these are free to view or download.
January 21, ORDcamp Ignite Talk Data Visualizations Done Wrong: live lecture recording. 5:11 (quick and fun)
January 25, Strata presentation preview Design Thinking for Effective Data Visualization: online lecture. 17:15
January 27, UIE Virtual Seminar preview podcast The Power of Data Visualization: audio recording, transcript. 29:50
February 2, UIE Virtual Seminar Telling the Right Story with Data Visualization: online lecture with slides; paid. 90 minutes. (With free preview. 1:08)
February 23, Visual.ly blog guest post Why Is Data Visualization So Hot?
March 16, UIE Virtual Seminar follow up podcast: audio recording, transcript. 30:38
April 2, Where conference interviewed by Julie Steele: video. 11:55
April 3, Where conference keynote When Not to Use Maps, live lecture recording. 11:32 (this one is fun too)
April 4, Linked In Tech Talk Designing Data Visualizations, live lecture recording, with extra conversation about visualization of social networks. 1:49:12
July 12, dotAstronomy conference keynote How to be a Data Visualization Star, with Julie Steele: live lecture audio recording, with still images, slides, and time-coordinated notes twitter responses. 53:05
July 16, European Bioinformatics Institute guest lecture Designing Effective Data Visualizations, handouts, sketchnotes, slide decks (I love the sketchnotes).
Here’s my table of properties and best uses of visual encodings. Feel free to download the PDF.
Update September 25, 2013: I wrote a whitepaper for IBM that explains the use of the table.
Please note that I’m not the first person to create a table like this, this just happens to be my take on it. I got my first glimpse at this sort of table in Jock Mackinlay‘s PhD thesis from 1986.
A really nice table comparing inclusion of visual properties/attributes in tables has been put together by Richard Brath.
After seeing a poor infographic re-posted, I briefly ranted on twitter:
Terrible presentation of data. The bars are OK, the rest is worthless. http://bit.ly/o76BOm @litmusapp #fb #li
I don’t mean to be harsh, but #infovis culture needs constructive critique to improve, not just reposting. /cc @visualizingOrg @litmusapp
To put my mouse where my mouth (well, keyboard) is, I present two quick & dirty re-drawings, with commentary, of the first two sections of the poor infographic.
In both of these sections, an aesthetically pleasing, but not-very-functional, donut graph has been used. In both cases, it’s the wrong choice, though for different reasons.
I’m thrilled to announce my latest book, Designing Data Visualizations.
The goal of this book is to teach you the process of designing a visualization, presenting the important considerations, and informing the choices that you make. It’s tool-agnostic, and is entirely applicable to visualizations with high and low volumes of data, and to both quantitative and qualitative visualizations.
The origins of my thinking on this topic lie in the work I did on my master’s thesis. This book benefits from an additional five years of experience and research on my part, as well as Julie’s vital insight, knowledge, and contributions.
For this book, we (the authors) are recommending the electronic version over the print version, as that will allow you easy access to updates and revisions as we add more examples and such. Also, the print edition will sadly be only in grayscale, whereas the electronic version will be full-color.
Update: looks like we’ll be providing a full-color download of the images so that people who buy the print edition don’t miss anything.
We’re very excited about this book. We hope you will be too.
I’ll be speaking at Strata NY next week. My main talk is Designing Data Visualizations, on Friday the 23rd at 11:30am.
On Friday at 1pm I’ll be at the O’Reilly booth, signing copies of Beautiful Visualization. Not sure if Designing Data Visualizations will be available in print by then, but if it is, I’ll be signing those too. (You probably want the electronic version of DDV anyway.)
Hope to see you there!
Here’s the podcast and transcript that goes with my virtual seminar on visualization for UIE. In this podcast I talk a lot about basic visualization concepts, with some good Q&A. It’s good foundational material.
Chapter One: On Beauty, by Noah Iliinsky
This chapter is an examination of what we mean by beauty in the context of visualization, why it’s a worthy goal to pursue, and how to get there. We’ll start with a discussion of the elements of beauty, look at some examples and counterexamples, and then focus on the critical steps to realize a beautiful visualization.
[I use the words visualization and visual interchangeably in this chapter, to refer to all types of structured representation of information. This encompasses graphs, charts, diagrams, maps, storyboards, and less formally structured illustrations.]
What is Beauty?
What do we mean when we say a visual is beautiful? Is it an aesthetic judgment, in the traditional sense of the word? It can be, but when we’re discussing visuals in this context, beauty can be considered to have four key elements, of which aesthetic judgment is only one. For a visual to qualify as beautiful, it must be aesthetically pleasing, yes, but it must also be novel, informative, and efficient.
For a visual to truly be beautiful, it must go beyond merely being a conduit for information and offer some novelty: a fresh look at the data or a format that gives readers a spark of excitement and results in a new level of understanding. Well-understood formats (e.g., scatterplots) may be accessible and effective, but for the most part they no longer have the ability to surprise or delight us. Most often, designs that delight us do so not because they were designed to be novel, but because they were designed to be effective; their novelty is a byproduct of effectively revealing some new insight about the world. keep reading…